Issue #264
November 27, 2024
Welcome to the Thanksgiving edition of the Data Science Briefing!
The next edition of the LLMs for Data Science webinar is coming up on lucky Friday, the 13th. Register now!
We’re also proud to announce that the next edition of the LangChain for Generative AI Pipelines webinar has just been confirmed for Jan 15, 2025 and you can already register!
In our regularly scheduled content, we learn how OpenAI, Google and Anthropic Are Struggling to Build More Advanced AI, wonder if Large Language Models learn world models or just surface statistics and dive into Bayesian Neural Networks and Personality Basins.
On the academic front, we ponder Human-AI coevolution, how to use Large language models (LLMs) as agents for augmented democracy and why static network structures cannot stabilize cooperation among Large Language Model agents.
This week’s book recommendation is “Life as No One Knows It: The Physics of Life’s Emergence” by S. I. Walker. You can find all the previous book recommendations on our website. In this week’s video, we have a tutorial on How to use Streamlit in 30 Minutes.
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Semper discentes,
The D4S Team
This weeks book is "Life as No One Knows It: The Physics of Life's Emergence" by S. I. Walker. In this book, Walker offers a groundbreaking reimagining of life’s nature, challenging traditional definitions and exploring the origins of living systems from a fresh perspective. At the heart of the book lies Assembly Theory, which proposes that all matter can be viewed as information, with life representing a highly complex assembly of causal information. This paradigm-shifting framework not only redefines life universally but also paves the way for identifying non-terrestrial life forms.
In an interdisciplinary approach that blends insights from Physics and Philosophy, the book explores key distinctions between knowledge, information, and consciousness. It encourages readers to move beyond anthropocentric perspectives, prompting us to consider the existence of life forms vastly different from anything encountered on Earth.
- 1. OpenAI, Google and Anthropic Are Struggling to Build More Advanced AI [bloomberg.com]
- 2. A Short Introduction to Automotive Lidar Technology [viksnewsletter.com]
- 3. Do Large Language Models learn world models or just surface statistics? [thegradient.pub]
- 4. Bayesian Neural Networks [cs.toronto.edu]
- 5. Personality Basins [near.blog]
- 6. PyTorch 101, Understanding Graphs, Automatic Differentiation and Autograd [digitalocean.com]
- 7. You could have designed state of the art positional encoding [fleetwood.dev]
- • Human-AI coevolution (D Pedreschi, L. Pappalardo, E. Ferragina, R. Baeza-Yates, A.-L. Barabási, F. Dignum, V. Dignum, T. Eliassi-Rad, F. Giannotti, J. Kertész, A. Knott, Y. Ioannidis, P. Lukowicz, A. Passarella, A. S. Pentland, J. Shawe-Taylor, A. Vespignani)
- • Population connectivity shapes the distribution and complexity of chimpanzee cumulative culture (C. Gunasekaram, F. Battiston, O. Sadekar, C. Padilla-Iglesias, M. A. van Noordwijk, R. Furrer, A. Manica, J. Bertranpetit, A. Whiten, C. P. van Schaik, L. Vinicius, A. B. Migliano)
- • Large language models (LLMs) as agents for augmented democracy (J. F. Gudiño, U. Grandi, C. Hidalgo)
- • Susceptibility to online misinformation: A systematic meta-analysis of demographic and psychological factors (M. Sultan, A. N. Tump, N. Ehmann, P. Lorenz-Spreen, R. Hertwig, A. Gollwitzer, R. H. J. M. Kurvers)
- • Limited trust in social network games (T. Murray, J. Garg, R. Nagi)
- • Funnel theorems for spreading on networks (G. Fibich, T. Levin, S. Schochet)
- • Static network structure cannot stabilize cooperation among Large Language Model agents (J. Han, B. Battu, I. Romić, T. Rahwan, P. Holme)
How to use Streamlit in 30 Minutes
All our videos are also available in our
YouTube playlist.
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